粒子群优化的门控循环单元网络漂流浮标轨迹预测  

Gated Recurrent Unit Network of Particle Swarm Optimization for Drifting Buoy Trajectory Prediction

作　　者：刘凇佐 王虔[1,2,3] 李磊 李慧[6] 余赟[7] LIU SongZuo;WANG Qian;LI Lei;LI Hui;YU Yun(National Key Laboratory of Underwater Acoustic Technology,Harbin Engineering University,Harbin,150001,China;Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University),Ministry of Industry and Information Technology,Harbin,150001,China;College of Underwater Acoustic Engineering,Harbin Engineering University,Harbin 150001,China;Sanya Nanhai Innovation and Development Base of Harbin Engineering University,Sanya,572024,China;College of Oceanography and Space Informatics,China University of Petroleum(East China),Qingdao 266580,China;College of Intelligent Systems Science and Engineering,Harbin Engineering University Harbin 150001,China;Naval Academy of Armament,Beijing 100161,China)

机构地区：[1]哈尔滨工程大学水声技术重点实验室,哈尔滨150001 [2]工业和信息化部海洋信息获取与安全工信部重点实验室(哈尔滨工程大学),哈尔滨150001 [3]哈尔滨工程大学水声工程学院,哈尔滨150001 [4]哈尔滨工程大学三亚南海创新发展基地,三亚572024 [5]中国石油大学(华东)海洋与空间信息学院,青岛266580 [6]哈尔滨工程大学智能科学与工程学院,哈尔滨150001 [7]海军装备研究院,北京100161

出　　处：《电子与信息学报》2024年第8期3295-3304,共10页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61803115)。

摘　　要：该文针对漂流浮标的轨迹预测问题,提出一种基于深度学习框架的端对端预测模型。由于不同海域的水动力模型存在较大差异,针对海面漂流浮标的流体载荷计算也较为复杂。因此,该文根据漂流浮标历史轨迹形成的多维时间序列,提出更具有普适性的基于数据驱动的轨迹预测模型。该模型将粒子群优化算法(PSO)与门控循环单元(GRU)结合,使用PSO算法对GRU神经网络的超参数进行初始化,经过多次迁移迭代训练后获得最优漂流浮标轨迹预测模型。最后使用多个北大西洋真实漂流浮标轨迹数据进行验证,结果表明PSOGRU算法能够实现准确的漂流浮标轨迹预测。Considering the trajectory prediction problem of drift buoys,an end-to-end prediction model based on the depth learning framework is proposed in this paper.The hydrodynamic models in different sea areas are quite different,and the calculation of fluid load of floating buoys on the sea surface is also complicated.Therefore,a more universal data-driven trajectory prediction model based on the multidimensional time series formed by the historical trajectories of drifting buoys is proposed.In this model,Particle Swarm Optimization(PSO)is combined with Gated Recurrent Unit(GRU),and the PSO is used to initialize the hyperparameters of the GRU neural network.The optimal drifting buoy trajectory prediction model is obtained after multiple migration iteration training.Finally,several real drifting buoy track data in the North Atlantic are used to verify the results.The results show that the PSOGRU algorithm can achieve accurate drifting buoy track prediction results.

关 键 词：漂流浮标 轨迹预测 粒子群优化 门控循环单元 

分 类 号：TN96[电子电信—信号与信息处理]